Evaluation of Changes in Prices and Purchases Following Implementation of Sugar-Sweetened Beverage Taxes Across the US

Key Points Question What changes occurred in sugar-sweetened beverage (SSB) prices and purchase volume after SSB taxes were implemented in 5 large US cities? Findings In this cross-sectional study, SSB taxes in Boulder, Colorado; Philadelphia, Pennsylvania; Oakland, California; San Francisco, California; and Seattle, Washington, were associated with a 33.1% composite increase in SSB prices (92% pass-through of taxes to consumers) and a 33% reduction in purchase volume, without increasing cross-border purchases. The results were sustained in the months following tax implementation. Meaning The results suggest substantial, consistent declines in SSB purchases across several US cities; insofar as reducing SSB consumption can improve population health, scaling SSB taxes more broadly should be considered.


Augmented Synthetic Control Model
3] These models are advantageous because they algorithmically create a counterfactual unit that can be directly compared to a treated unit of interest without worrying about fundamental differences in outcomes or characteristics of the two groups, by construction.Using our setting as an example, the base synthetic control model matches pretax outcomes and covariates of taxed and untaxed units by weighting each untaxed unit in such a way that the "synthetic" unit(s) closely match the taxed unit(s) on both the outcome measure of interest and covariate characteristics.In particular, for each outcome   for 3-digit zip code  in month , and   3-digit zip code-level covariates (which in our case are time-invariant), the method chooses weights for each untaxed 3-digit zip code  (  ) to minimize the distance (  ,   ) − ∑   �  ,   �  . 46][7] There are two notable enhancements of the original synthetic control method that we leverage in this study.First, the original synthetic control framework was designed to estimate the impact of an intervention on a single treated unit.In our setting, we study multiple treated units that experience treatment at different times, referred to as a "staggered adoption" setup. 8Second, the use of the original synthetic control method was recommended only when the synthetic unit's pretreatment outcomes closely matched the pre-treatment outcomes of the treated unit.
Our study takes advantage of recent work that relaxes this requirement by introducing a "biascorrection" procedure.This estimation framework is called the augmented synthetic control (ASC) model, since it augments the original synthetic control approach with an outcome model that is used to determine bias as a result of a relatively poor pretreatment fit between the treated and synthetic units, and then uses the output to remove the bias in the pretreatment period. 9While there are several different outcome models that can be used to de-bias the synthetic control model, the primary method used is a ridge regression model. 9A ridge regression model estimates a linear regression of post-treatment outcomes of the control units (  | ≥ ), where  indicates the month of tax implementation, on the centered pre-treatment outcomes of the control units (  | < ).This modification allows certain donor units to be assigned negative weights (whereas the original synthetic control procedure restricts all weights on donor units to be ≥ 0), which can improve pretreatment fit.Additional structure and details of this procedure can be found in sections 2-4 of Ben-Michael, Feller, and Rothstein (2021).
Sociodemographic and geographic characteristics used in constructing synthetic units were taken from the 2010 Decennial Census and 2016 American Community Survey.Characteristics included population size (2010), median household income (2016), racial/ethnic composition (proportion non-Hispanic White, non-Hispanic Black, Hispanic, Asian, and American Indian/Alaska Native 2010), proportion in poverty (household income <$10,000K, 2016), proportion of individuals 18 to 64 years old (2010), number of housing units (2010), and percentage of the population defined as urban (2010).
One important implication of the use of synthetic control methods is the importance of a donor pool consisting of units that could plausibly act as reasonable controls for the treated units. 4ilure to do so can lead to substantial bias in the estimation.Because of this, we decide to limit the donor pool of 3-digit zip codes to those with urbanicity levels that are similar to the treated units.Using a measure of urbanicity is desirable for different reasons.First, it's easily defined by and computed using information and data from the US Census. 10Second, urbanicity captures several observed and unobserved characteristics that are likely to influence the relative similarity among control and treated units, including characteristics we include like population, median household income, number of housing units, etc., as well as characteristics we do not observe, like housing prices, police presence, and voter party alignment.Finally, our five treated localities have an average urbanicity level of 0.98, which ranges from a minimum of 0.94 (Boulder) to 1 (Philadelphia and San Francisco).This relative similarity between the treated units' urbanicity allows for the construction of a donor pool that could plausibly act as reasonable controls for each of the treated units, while keeping the donor pool the same for each.
In the primary augmented synthetic control estimation, we use a subsample of control (donor) units that fall within one standard deviation of the average urbanicity level of the five treated localities.In the cross-border shopping analyses, we use a subsample of donor units that fall within one standard deviation of the average urbanicity level of the thirteen adjacent border localities.Robustness checks, which are included in eFigures 10-15, include control units with urbanicity levels >0.85 and >0.9.The results from these supplementary estimations are qualitatively unchanged.
Because implementation of the tax happened at different times across the five treated localities (hence the "staggered adoption" nature of the BCSC procedure), calendar time is converted to event-time, which normalizes time = 0 to the month when the tax went into place in each treated locality.Therefore, in event time we observe a different number of total time periods for each taxed locality.Consequently, we provide results from a "balanced" estimation, which only considers event-time periods when all treated localities are present in the sample.This is done to avoid biasing the estimation in favor of taxed localities that are observed in the data during event-time periods when other taxed localities may not be observed.
To determine the statistical significance of our augmented synthetic control average treatment effects, which are calculated as the average post-tax percent change in SSB purchases (shelf prices), we use an in-space placebo generation inference procedure. 3,11For each of the five treated localities, we generate in-space placebo estimates for each donor pool unit one-by-one as if each unit had been treated.Because treated localities implement taxes at different times, we repeat this procedure for each of the five different treated localities, which generates 279*5=1,395 placebo estimates.To generate p-values, we compute the ratio of mean squared prediction error (RMSPE) in the post-tax vs. pre-tax period for the composite unit estimate and each of the placebo unit estimates, and rank them from largest to smallest. 11The p-value for the estimation is calculated as the ratio of the composite unit numerical ranking with respect to the total number of units (1,396).Each of the BCSC plots takes 100 quasi-randomly selected placebo lines from the universe of 1,396 placebos for the composite estimation and 279 for each of the individual city estimations.This selection procedure is quasi-random in the sense that the universe of eligible placebos to be chosen is "pruned" to those that exhibit a preperiod MSPE that is no greater than five times the pre-period MSPE of the treated unit.
Confidence intervals were obtained from p-values using the method outlined by Altman. 12

Two-Way Fixed Effects (TWFE) Model
Using this conventional approach, we estimate a series of TWFE models and TWFE event study models.The simple TWFE model takes the following form: Where   indicates the outcome variable of interest (i.e.volume purchased or shelf prices) in 3digit zip code  in month-year ,   is a binary variable =1 if 3-digit zip code  has an SSB tax in place during month-year  and =0 otherwise, and   and   represent 3-digit zip code and month-year fixed effects, respectively. measures the treatment effect associated with the implementation of an SSB tax on the outcome variable of interest.We estimate such a TWFE model to determine both the composite effect (by including all five treated 3-digit zip codes) as well as individual city effects (separate estimations for each of the five treated 3-digit zip codes).eTable 3 presents the TWFE estimates for each of these specifications.
We also estimate a TWFE event study specification, which estimates individual coefficients for each month-year in event-time, which is normalized to 0 at the month-year when an SSB tax is implemented in 3-digit zip code .Again, we estimate a TWFE event study to determine both the composite effect (by including all five treated 3-digit zip codes) as well as individual city Note: Population estimates for each city taken from 2010 (source: US Census Bureau).In the "981" 3-digit zip code, some untaxed cities (e.g.Bainbridge Island) overlap with other untaxed 3-digit zip codes (e.g.Bainbridge Island includes areas in the 980 and 983 zip codes).Therefore, population estimates for untaxed cities in the "981" 3-digit zip code may include people from untaxed 3-digit zip codes.Because of this, the estimate of the % of the population covered by an SSB tax in the "981" 3-digit zip code is conservative (underestimated).

eFigure 1 . 2 .
Comparing Treated and Synthetic Values of Prognostic Factors from the Analysis of SSB Shelf Prices eFigure Overlap of US Census Sociodemographic Characteristics between each taxed city and the donor pool of control 3-digit zip codes Note: Metrics for each 3-digit zip code were taken from either the 2010 Census or 2016 American Community Survey (ACS).Colored points on each plot represent values for each of the five treated localities.Box plots for each characteristic are formed from the distribution within the subsample of 3-digit zip codes used in the primary analysis.

eFigure 3 .
Composite and Individual Locality Price Pass-ThroughNote: Coefficient estimates represent the average total number of cents per ounce passed through to shelf prices of SSB products in the composite estimation and each individual treated locality.Dotted red lines denote full (100%) pass-through.Lightly shaded horizontal lines through each coefficient indicate 95% confidence intervals.% pass-thru indicates the % of the per-ounce tax in the composite estimation and each individual treated locality that was reflected in changes in shelf prices.

eFigure 4 . 5 .eFigure 6 .eFigure 7 .
Composite and Individual Changes in Volume Sales in Adjacent Border Zip Codes Note: Coefficient estimates represent the % change in SSB purchases in immediately adjacent border localities to each treated locality, and all borders in the composite estimation.Lightly shaded horizontal lines through each coefficient indicate 95% confidence intervals.Corresponding 95% confidence intervals and p-values are indicated next to each coefficient.eFigure Augmented Synthetic Control Estimates for Individual Locality Changes in Price (a) 803 (Boulder) (c) 191 (Philadelphia) (e) 981 (Seattle) (b) 941 (San Francisco) (d) 946 (Oakland) © 2024 Kaplan S et al.JAMA Health Forum.Augmented Synthetic Control Estimates for Individual Locality Changes in Volume Sales (a) 803 (Boulder) (c) 191 (Philadelphia) (e) 981 (Seattle) (b) 941 (San Francisco) (d) 946 (Oakland) © 2024 Kaplan S et al.JAMA Health Forum.Augmented Synthetic Control Estimates of Individual Locality Changes in Volume Sales of SSB Products in Border Areas (a) 803 (Boulder) (c) 191 (Philadelphia) (e) 981 (Seattle) (b) 941 (San Francisco) (d) 946 (Oakland) © 2024 Kaplan S et al.JAMA Health Forum.

eFigure 8 .
Augmented Synthetic Control Estimates for Composite Changes in Price and Volume Sales of SSB Products (Population Weighted) Panel A. Changes in SSB Prices Panel B. Changes in SSB Volume Sales Note: Panel a) shows the % change in volume sold (in ounces), and panel b) the % change in shelf prices in response to implementing an excise SSB tax for the staggered adoption composite analysis.The bolded purple line represents the composite treated unit, while the lightly shaded grey lines represent in-space placebo estimates from the donor pool.The composite effect is explicitly weighted by the population of each individual treated city.% changes are calculated with respect to the population-weighted average of the pre-treatment means of each of the five treated localities.The composite effect size estimates and p-values are provided in the designated box of each panel.© 2024 Kaplan S et al.JAMA Health Forum.

eFigure 9 .eFigure 10 .eFigure 13 .
Augmented Synthetic Control Estimates of Composite Changes in Volume Sales of SSB Products in Border Areas (Population Weighted) Note: This figure shows the staggered adoption composite analysis % change in volume sold (in ounces) in immediately adjacent bordering 3-digit zip codes in response to implementing an excise SSB tax in the five treated zip codes.The bolded purple line represents the composite adjacent border unit, while the lightly shaded grey lines represent in-space placebo estimates from the donor pool.The composite effect is explicitly weighted by the population of each individual treated city.% changes are calculated with respect to the population-weighted average of the pre-treatment means of each of the twelve adjacent border localities.The composite effect size estimates and p-values are provided in the designated box.Composite and Individual Locality Demand Elasticity Estimates (Urbanicity > 0.85) Note: This plot shows the % change in volume sold (in ounces) and % change in price for the augmented synthetic control composite analysis, and the same information for augmented synthetic control analyses of each of the five treated localities individually.Price elasticities of demand are provided in brackets, and 95% confidence intervals and p-values for each estimation are provided in parentheses.eFigure 11.Augmented Synthetic Control Estimates for Composite Changes in Price and Volume Sales of SSB Products (Urbanicity > 0.85) Panel A. Changes in SSB Prices Panel B. Changes in SSB Volume Sales Note: Panel a) shows the % change in price and panel b) the % change in volume in response to the implementation of an excise SSB tax for the composite analysis.The bolded purple line represents the composite treated unit, while the lightly shaded grey lines represent in-space placebo estimates from the donor pool.% changes are calculated with respect to the average of the pre-treatment means of each of the five treated localities.The average composite effect estimates and p-values are provided in the designated box of each panel.eFigure12. Augmented Synthetic Control Estimates of Composite Changes in Volume Sales of SSB Products in Border Areas (Urbanicity > 0.85) Note: This figure shows the composite analysis % change in volume sold in immediately adjacent bordering 3-digit zip codes in response to the implementation of an excise SSB tax in the five treated zip codes.The bolded purple line represents the composite adjacent border unit, while the lightly shaded grey lines represent in-space placebo estimates from the donor pool.% changes are calculated with respect to the average of the pre-treatment means of each of the twelve adjacent border localities.The average composite effect estimates and p-values are provided in the designated box.Composite and Individual Locality Demand Elasticity Estimates (Urbanicity > 0.9) Note: This plot shows the % change in volume sold (in ounces) and % change in price for the augmented synthetic control staggered adoption composite analysis, and the same information for augmented synthetic control analyses of each of the five treated localities individually.Price elasticities of demand are provided in brackets, and 95% confidence intervals and p-values for each estimation are provided in parentheses.© 2024 Kaplan S et al.JAMA Health Forum.eFigure14. Augmented Synthetic Control Estimates for Composite Changes in Price and Volume Sales of SSB Products (Urbanicity > 0.9) Panel A. Changes in SSB Prices Panel B. Changes in SSB Volume Sales Note: Panel a) shows the % change in price and panel b) the % change in volume sold (in ounces) in response to the implementation of an excise SSB tax for the composite analysis.The bolded purple line represents the composite treated unit, while the lightly shaded grey lines represent in-space placebo estimates from the donor pool.% changes are calculated with respect to the average of the pre-treatment means of each of the five treated localities.The average composite effect estimates and p-values are provided in the designated box of each panel.© 2024 Kaplan S et al.JAMA Health Forum.eFigure 15.Augmented Synthetic Control Estimates of Composite Changes in Volume Sales of SSB Products in Border Areas (Urbanicity > 0.9) Note: This figure shows the composite analysis % change in volume sold in immediately adjacent bordering 3-digit zip codes in response to the implementation of an excise SSB tax in the five treated zip codes.The bolded purple line represents the composite adjacent border unit, while the lightly shaded grey lines represent in-space placebo estimates from the donor pool.% changes are calculated with respect to the average of the pre-treatment means of each of the twelve adjacent border localities.The average composite effect estimates and p-values are provided in the designated box.eFigure 16.TWFE Estimates of Composite Changes in Prices, Volume Sales, and Border Volume Sales Panel A. Changes in SSB Prices Panel B. Changes in SSB Volume Sales Panel C. Changes in Border SSB Volume Sales Note: All point estimates should be interpreted relative to the omitted event-time period (-1).95% CIs are depicted with each point estimate.The red dashed line indicates timing of policy enactment.eFigure 17.TWFE Estimates of Individual Locality Changes in Prices (a) 803 (Boulder) (c) 191 (Philadelphia) (e) 981 (Seattle) (b) 941 (San Francisco) (d) 946 (Oakland) Note: All point estimates should be interpreted relative to the omitted event-time period (-1).95% CIs are depicted with each point estimate.The red dashed line indicates timing of policy enactment.eFigure 18. TWFE Estimates of Individual Locality Changes in Volume Sales (a) 803 (Boulder) (c) 191 (Philadelphia) (e) 981 (Seattle) (b) 941 (San Francisco) (d) 946 (Oakland) Note: All point estimates should be interpreted relative to the omitted event-time period (-1).95% CIs are depicted with each point estimate.The red dashed line indicates timing of policy enactment.eFigure 19.TWFE Estimates of Individual Locality Changes in Volume Sales of SSB Products in Border Areas (a) 803 (Boulder) (c) 191 (Philadelphia) (e) 981 (Seattle) (b) 941 (San Francisco) (d) 946 (Oakland) Note: All point estimates should be interpreted relative to the omitted event-time period (-1).95% CIs are depicted with each point estimate.The red dashed line indicates timing of policy enactment.
2024 Kaplan S et al.JAMA Health Forum.effects (separate estimations for each of the five treated 3-digit zip codes).The TWFE eventstudy model takes the following form:  , +   +   +   Where  represents the month-year in event-time, ranging from − to .The period prior to implementation of an SSB tax (-1) is omitted.  is a vector of coefficients indexed by eventtime that can be interpreted relative to the omitted event-time period. , = 1 if 3-digit zip code  has been treated at event-time .eFigure 16 presents the event study results for the composite estimation, while eFigures 17-19 present the event study results for the individual city estimations.eTable 1.Total Coverage of SSB Ounces Sold in Matched Nielsen Retail Scanner Data Note: Tax revenues taken from Krieger et al. (2021). 1 Coverage estimates use the first fiscal year of each city's respective tax implementation.Lower coverage in Philadelphia is in part due to the exclusion of artificially sweetened beverages in our analysis.The tax amount for the Composite geographic unit is the unweighted average of the tax amounts across the five taxed cities.Total Population (2010) by City within Taxed 3-Digit Zip Codes 1Krieger J, Magee K, Hennings T, Schoof J, Madsen KA.How sugar-sweetened beverage tax revenues are being used in the United States.Preventive Medicine Reports.2021 Sep 1;23:101388.
Two-Way Fixed Effects Estimation Results for Composite and Individual City Analyses Note: * p<0.05 ** p<0.01 *** p<0.001All specifications include 3-digit zip code and month-year fixed effects.Standard errors are robustly estimated and clustered at the 3-digit zip code level.